Skip to main content
Log in

Non-contact optical dynamic measurements at different ranges: a review

  • Review Paper
  • Published:
Acta Mechanica Sinica Aims and scope Submit manuscript

Abstract

Optical dynamic measurements are widely used for non-contact vibration, continuous deformation, or moving objects. Various measurement techniques were developed for different deformation amplitudes. This paper reviews three types of technique for different measurement ranges: interferometric techniques for deformation or vibration (nanometer to sub-millimeter amplitude) whose measurement accuracies rely on phase extraction of interferometric signal; imaging based techniques for deformation or vibration (micrometer to centimeter amplitude) with the aid of moiré, structured light, and man-made speckles, whose sensitivities is from 1/100 to 1/10 pixel; and videometrics for large deformation or movement detection (greater than centimeter amplitude). Many research groups have improved measurement capabilities for these three techniques to meet particular industrial application requirements.

Graphic abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Cloud, G.L.: Optical Methods of Engineering Analysis. Cambridge University Press, New York (1995)

    Google Scholar 

  2. Dally, J.W., Riley, W.F.: Experimental Stress Analysis, 3rd edn. McGraw-Hill, New York (1991)

    Google Scholar 

  3. Palevicius, P., Aleksa, A., Maskeliunas, R., et al.: Circular geometric moiré for degradation prediction of mechanical components performing angular oscillations. Mech. Syst. Signal Process. 86, 278–285 (2017)

    Google Scholar 

  4. Zhang, W., Zhu, F., Wang, S., et al.: An accurate method to calibrate shadow moiré measurement sensitivity. Meas. Sci. Technol. 30, 125021 (2019)

    Google Scholar 

  5. Tang, Y., Yao, J., Chen, J.: Novel method for increasing accuracy of projection moiré contouring of large surfaces. Opt. Express 24, 21190–21204 (2016)

    Google Scholar 

  6. Vest, C.M.: Holographic Interferometry. Wiley, New York (1979)

    Google Scholar 

  7. Jones, R., Wykers, C.: Holographic and Speckle Interferometry, 2nd edn. Cambridge University Press, Cambridge (1989)

    Google Scholar 

  8. Hung, Y.Y.: Shearography: a new optical method for strain measurement and nondestructive testing. Opt. Eng. 21, 391–395 (1982)

    Google Scholar 

  9. Post, D.: Chapter 7: moiré interferometry. In: Kobayashi, A. (ed.) Handbook on Experimental Mechanics. Prentice Hall, Englewood Cliffs (1987)

    Google Scholar 

  10. Zuo, C., Feng, S., Huang, L., et al.: Phase shifting algorithms for fringe projection profilometry: a review. Opt. Lasers Eng. 109, 23–59 (2018)

    Google Scholar 

  11. Schnars, U., Jueptner, W.: Digital Holography. Springer, Berlin (2005)

    Google Scholar 

  12. Creath, K.: Phase-shifting speckle interferometry. Appl. Opt. 24, 3053–3085 (1985)

    Google Scholar 

  13. Lv, C., Wang, K., Gu, G., et al.: Accurate full-edge detection and depth measurement of internal defects using digital speckle pattern interferometry. NDT E Int. 102, 1–8 (2019)

    Google Scholar 

  14. Pan, B.: Digital image correlation for surface deformation measurement: historical developments, recent advances and future goals. Meas. Sci. Technol. 29, 082001 (2018)

    Google Scholar 

  15. Fu, Y., Groves, R.M., Pedrini, G., et al.: Kinematic and deformation parameter measurement by spatiotemporal analysis of an interferogram sequence. Appl. Opt. 46, 8645–8655 (2007)

    Google Scholar 

  16. Kundu, S., Viswanadham, B.V.S.: Centrifuge modeling and DIC of dynamic compaction on sandy soils with shallow water table. J. Geotech. Geoenviron. Eng. 147, 04021037 (2021)

    Google Scholar 

  17. Fu, Y., Tay, C.J., Quan, C., et al.: Temporal wavelet analysis for deformation and velocity measurement in speckle interferometry. Opt. Eng. 43, 2780–2788 (2004)

    Google Scholar 

  18. Fu, Y., Shi, H., Miao, H.: Vibration measurement of miniature component by high-speed image-plane digital holographic microscopy. Appl. Opt. 48, 1990–1997 (2009)

    Google Scholar 

  19. Dong, C., Li, K., Jiang, Y., et al.: Evaluation of thermal expansion coefficient of carbon fiber reinforced composites using electronic speckle interferometry. Opt. Express 26, 531–543 (2018)

    Google Scholar 

  20. Tay, C.J., Fu, Y.: Determination of curvature and twist by digital shearography and wavelet transform. Opt. Lett. 30, 2873–2875 (2005)

    Google Scholar 

  21. Fu, Y., Pedrini, G., Osten, W.: Vibration measurement by temporal Fourier analyses of a digital hologram sequence. Appl. Opt. 46, 5719–5727 (2007)

    Google Scholar 

  22. Fu, Y., Pedrini, G., Hennelly, B.M., et al.: Dual-wavelength image-plane digital holography for dynamic measurement. Opt. Lasers Eng. 47, 552–557 (2009)

    Google Scholar 

  23. Li, F.C., Kishen, A.: Deciphering dentin tissue biomechanics using digital moiré interferometry: a narrative review. Opt. Lasers Eng. 107, 273–280 (2018)

    Google Scholar 

  24. Qian, K.M.: Two-dimensional windowed Fourier transform for fringe pattern analysis: principles, applications and implementations. Opt. Lasers Eng. 45, 304–317 (2007)

    Google Scholar 

  25. Wang, K., Dou, J., Qian, K., et al.: Y-Net: a one-to-two deep learning framework for digital holographic reconstruction. Opt. Lett. 44, 4765–4768 (2019)

    Google Scholar 

  26. Fu, Y., Tay, C.J., Quan, C., et al.: Wavelet analysis of speckle patterns with a temporal carrier. Appl. Opt. 44, 959–965 (2005)

    Google Scholar 

  27. Dong, J., Jia, S., Jiang, C.: Surface shape measurement by multi-illumination lensless Fourier transform digital holographic interferometry. Opt. Commun. 402, 91–96 (2017)

    Google Scholar 

  28. Kim, J.A., Kim, J.W., Kang, C.S., et al.: Interferometric profile scanning system for measuring large planar mirror surface based on single-interferogram analysis using Fourier transform method. Measurement 118, 113–119 (2018)

    Google Scholar 

  29. Xu, J., Kamada, Y., Takao, M., et al.: Experimental investigations of airfoil surface flow of a horizontal axis wind turbine with LDV measurements. Energy 191, 116558 (2020)

    Google Scholar 

  30. Ichikawa, Y., Koike, S., Nakakita, K.: Measurement of a flow-velocity profile using a laser Doppler velocimetry coupled with a focus tunable lens. OSA Contin. 3, 1781–1791 (2020)

    Google Scholar 

  31. Liu, C., Zang, C., Zhou, B.: A novel algorithm for determining the pose of a scanning laser Doppler vibrometer. Meas. Sci. Technol. 31, 025202 (2019)

    Google Scholar 

  32. Ngoi, B.K., Venkatakrishnan, K., Tan, B., et al.: Two-axis-scanning laser Doppler vibrometer for microstructure. Opt. Commun. 182, 175–185 (2000)

    Google Scholar 

  33. Yang, C., Guo, M., Liu, H., et al.: A multi-point laser Doppler vibrometer with fiber-based configuration. Rev. Sci. Instrum. 84, 121702 (2013)

    Google Scholar 

  34. Zhong, Y., Zhang, G., Leng, C., et al.: A differential laser Doppler system for one-dimensional in-plane motion measurement of MEMS. Measurement 40, 623–627 (2007)

    Google Scholar 

  35. Pieczonka, Ł, Ambroziński, Ł, Staszewski, W.J., et al.: Damage detection in composite panels based on mode-converted Lamb waves sensed using 3D laser scanning vibrometer. Opt. Lasers Eng. 99, 80–87 (2017)

    Google Scholar 

  36. Bhowmik, B., Tripura, T., Hazra, B., et al.: Real time structural modal identification using recursive canonical correlation analysis and application towards online structural damage detection. J. Sound Vib. 468, 115101 (2020)

    Google Scholar 

  37. Yang, C., Fu, Y., Yuan, J., et al.: Damage identification by using a self-synchronizing multipoint laser Doppler vibrometer. Shock Vib. (2015). https://doi.org/10.1155/2015/476054

    Article  Google Scholar 

  38. Acosta, L.S., Santoyo, F.M., Manuel, H., et al.: Study of skin rigidity variations due to UV radiation using digital holographic interferometry. Opt. Lasers Eng. 126, 105909 (2020)

    Google Scholar 

  39. Frankovský, P., Brodnianská, Z., Bocko, J., et al.: Application of holographic interferometry in the analysis of stress states in a crack root area. Appl. Opt. 59, D170–D178 (2020)

    Google Scholar 

  40. Thomas, B.P., Annamala, P.S., Narayanamurthy, C.S.: Investigation on vibration excitation of debonded sandwich structures using time-average digital holography. Appl. Opt. 56, F7–F13 (2017)

    Google Scholar 

  41. Casavola, C., Pappalettera, G.: Strain field analysis in electronic components by ESPI: bad thermal contact and damage evaluation. J. Nondestruct. Eval. 37, 1–7 (2018)

    Google Scholar 

  42. Toh, S., Shang, H., Chau, F., et al.: Flaw detection in composites using time-average shearography. Opt. Laser Technol. 23, 25–30 (1991)

    Google Scholar 

  43. Ma, Y., Jiang, H., Dai, M., et al.: Cantilevered plate vibration analysis based on electronic speckle pattern interferometry and digital shearing speckle pattern interferometry. Acta Opt. Sin. 39, 56–64 (2019). ((in Chinese))

    Google Scholar 

  44. De, G.D., Soons, J., Dirckx, J.J.: Digital stroboscopic holography setup for deformation measurement at both quasi-static and acoustic frequencies. Int. J. Optomechatron. 8, 275–291 (2014)

    Google Scholar 

  45. Pires, F., Muyshondt, P.G., Keustermans, W., et al.: Structural intensity analysis of flat plates based on digital stroboscopic holography measurements. J. Sound Vib. 428, 168–178 (2018)

    Google Scholar 

  46. Ebrahimian, A., Tang, H., Furlong, C., et al.: Material characterization of thin planar structures using full-field harmonic vibration response measured with stroboscopic holography. Int. J. Mech. Sci. 198, 106390 (2021)

    Google Scholar 

  47. Pedrini, G., Pfister, B., Tiziani, H.: Double pulse-electronic speckle interferometry. J. Mod. Opt. 40, 89–96 (1993)

    Google Scholar 

  48. Pedrini, G., Osten, W., Gusev, M.E.: High-speed digital holographic interferometry for vibration measurement. Appl. Opt. 45, 3456–3462 (2006)

    Google Scholar 

  49. Lyu, L.F., Zhu, W.D.: Operational modal analysis of a rotating structure under ambient excitation using a tracking continuously scanning laser Doppler vibrometer system. Mech. Syst. Signal Process. 152, 107367 (2021)

    Google Scholar 

  50. Yuan, K., Zhu, W.D.: Estimation of modal parameters of a beam under random excitation using a novel 3D continuously scanning laser Doppler vibrometer system and an extended demodulation method. Mech. Syst. Signal Process. 155, 107606 (2021)

    Google Scholar 

  51. Huntley, J.M., Kaufmann, G.H., Kerr, D.: Phase-shifted dynamic speckle pattern interferometry at 1 kHz. Appl. Opt. 38, 6556–6563 (1999)

    Google Scholar 

  52. Kaufmann, G.H.: Nondestructive testing with thermal waves using phase-shifted temporal speckle pattern interferometry. Opt. Eng. 42, 2010–2015 (2003)

    Google Scholar 

  53. Chen, W., Quan, C., Tay, C., et al.: Quantitative detection and compensation of phase-shifting error in two-step phase-shifting digital holography. Opt. Commun. 282, 2800–2805 (2009)

    Google Scholar 

  54. Zhang, S.: Absolute phase retrieval methods for digital fringe projection profilometry: a review. Opt. Lasers Eng. 107, 28–37 (2018)

    Google Scholar 

  55. Du, Y., Feng, G., Li, H., et al.: Spatial carrier phase-shifting algorithm based on principal component analysis method. Opt. Express 20, 16471–16479 (2012)

    Google Scholar 

  56. Millerd, J., Brock, N., Hayes, J., et al.: Pixelated phase-mask dynamic interferometers. In: Fringe 2005, pp 640–647. Springer, Berlin (2006)

  57. He, X., Qian, K.: A comparative study on temporal phase unwrapping methods in high-speed fringe projection profilometry. Opt. Lasers Eng. 142, 106613 (2021)

    Google Scholar 

  58. Klein, C., Riton, J., Stoilov, N.: Multi-domain spectral approach for the Hilbert transform on the real line. arXiv preprint arXiv.2101, 02473 (2021)

  59. Quan, C., Fu, Y., Tay, C.J., et al.: Profiling of objects with height steps by wavelet analysis of shadow moiré fringes. Appl. Opt. 44, 3284–3290 (2005)

    Google Scholar 

  60. Joenathan, C., Franze, B., Haible, P., et al.: Large in-plane displacement measurement in dual-beam speckle interferometry using temporal phase measurement. J. Mod. Opt. 45, 1975–1984 (1998)

    Google Scholar 

  61. Joenathan, C., Franze, B., Haible, P., et al.: Novel temporal Fourier transform speckle pattern shearing interferometer. Opt. Eng. 37, 1790–1795 (1998)

    Google Scholar 

  62. Colonna De Lega, X.: Processing of Non-stationary Interference Patterns: Adapted Phase-Shifting Algorithms and Wavelet Analysis: Application to Dynamic Deformation Measurements by Holographic and Speckle Interferometry. Verlag nicht ermittelbar (1997)

  63. Federico, A., Kaufmann, G.H.: Robust phase recovery in temporal speckle pattern interferometry using a 3D directional wavelet transform. Opt. Lett. 34, 2336–2338 (2009)

    Google Scholar 

  64. Dirckx, J., Van, E.H., Decraemer, W., et al.: Performance and testing of a four channel high-resolution heterodyne interferometer. Opt. Lasers Eng. 47, 488–494 (2009)

    Google Scholar 

  65. Zheng, W., Kruzelecky, R.V., Changkakoti, R.: Multichannel laser vibrometer and its applications. In: Third International Conference on Vibration Measurements by Laser Techniques: Advances and Applications (1998)

  66. Fu, Y., Guo, M., Phua, P.B.: Spatially encoded multibeam laser Doppler vibrometry using a single photodetector. Opt. Lett. 35, 1356–1358 (2010)

    Google Scholar 

  67. Fu, Y., Guo, M., Phua, P.B.: Multipoint laser Doppler vibrometry with single detector: principles, implementations, and signal analyses. Appl. Opt. 50, 1280–1288 (2011)

    Google Scholar 

  68. Fu, Y., Guo, M., Phua, P.B.: Cross-talk prevention in optical dynamic measurement. Opt. Lasers Eng. 50, 547–555 (2012)

    Google Scholar 

  69. Rajic, N., Rosalie, C., Norman, P., et al.: Determination of the in-plane components of motion in a Lamb wave from single-axis laser vibrometry. J. Acoust. Soc. Am. 135, 3446–3454 (2014)

    Google Scholar 

  70. Lemistre, M., Balageas, D.: Structural health monitoring system based on diffracted Lamb wave analysis by multiresolution processing. Smart Mater. Struct. 10, 504 (2001)

    Google Scholar 

  71. Liu, Z., et al.: Simple and fast rail wear measurement method based on structured light. Opt. Lasers Eng. 49, 1343–1351 (2011)

    Google Scholar 

  72. Gu, F., Song, Z., Zhao, Z.: Single-shot structured light sensor for 3D dense and dynamic reconstruction. Sensors 20, 1094 (2020)

    Google Scholar 

  73. Zhang, P., et al.: High dynamic range 3D measurement based on structured light: a review. J. Adv. Manuf. Sci. Technol. 1, 2021004-1-2021004–9 (2021)

    Google Scholar 

  74. Setumin, S., Aminudin, M.F.C., Suandi, S.A.: Canonical correlation analysis feature fusion with patch of interest: a dynamic local feature matching for face sketch image retrieval. IEEE Access 8, 137342–137355 (2020)

    Google Scholar 

  75. Cheng, D.Z., Li, Y.J., Yu, R.X.: Image matching method based on improved SIFT algorithm. Comput. Simul. 28, 285–289 (2011)

    Google Scholar 

  76. Ma, J., et al.: Robust feature matching for remote sensing image registration via locally linear transforming. IEEE Trans. Geosci. Remote Sens. 53, 6469–6481 (2015)

    Google Scholar 

  77. Cui, H., Hu, Q., Mao, Q.: Real-time geometric parameter measurement of high-speed railway fastener based on point cloud from structured light sensors. Sensors 18, 3675 (2018)

    Google Scholar 

  78. Qin, G., Wang, X., Yin, L.: Calibration method for multi-line structured light vision sensor based on Plücker line. J. Meas. Sci. Instrum. 11, 103–111 (2020)

    Google Scholar 

  79. Lu, X.T., Wu, Q.Y., Huang, H.T.: Calibration based on ray-tracing for multi-line structured light projection system. Opt. Express 27, 35884–35894 (2019)

    Google Scholar 

  80. Wu, Q., Zou, W., Xu, D.: Viewpoint planning for freeform surface inspection using plane structured light scanners. Int. J. Autom. Comput. 13, 42–52 (2016)

    Google Scholar 

  81. Sun, C.R., Zhang, X.Y.: Real-time subtraction-based calibration methods for deformation measurement using structured light techniques. Appl. Opt. 58, 7727–7732 (2019)

    Google Scholar 

  82. Zuo, C., et al.: Temporal phase unwrapping algorithms for fringe projection profilometry: a comparative review. Opt. Lasers Eng. 85, 84–103 (2016)

    Google Scholar 

  83. Qian, J., Feng, S., Li, Y., et al.: Single-shot absolute 3D shape measurement with deep-learning-based color fringe projection profilometry. Opt. Lett. 45, 1842–1845 (2020)

    Google Scholar 

  84. Zhang, S.: Rapid and automatic optimal exposure control for digital fringe projection technique. Opt. Lasers Eng. 128, 106029 (2020)

    Google Scholar 

  85. Zuo, C., et al.: High-speed three-dimensional shape measurement for dynamic scenes using bi-frequency tripolar pulse-width-modulation fringe projection. Opt. Lasers Eng. 51, 953–960 (2013)

    Google Scholar 

  86. Feng, S.J., et al.: General solution for high dynamic range three-dimensional shape measurement using the fringe projection technique. Opt. Lasers Eng. 59, 56–71 (2014)

    Google Scholar 

  87. Qian, J., Feng, S., Xu, M., et al.: High-resolution real-time 360° 3D surface defect inspection with fringe projection profilometry. Opt. Lasers Eng. 137, 106382 (2021)

    Google Scholar 

  88. Liu, Y., Fu, Y., Cai, X., et al.: A novel high dynamic range 3D measurement method based on adaptive fringe projection technique. Opt. Lasers Eng. 128, 106004 (2020)

    Google Scholar 

  89. Cao, Y., Wang, S., Qi, S., et al.: Carrier fringe method of moiré interferometry for tiny strain measurements in micro-field. Acta Mech. Sin. 25, 101 (2009)

    Google Scholar 

  90. Jeong, M., Kim, S.: Color grating projection moiré with time-integral fringe capturing for high-speed 3-D imaging. Opt. Eng. 41, 1912–1918 (2002)

    Google Scholar 

  91. Chen, L., Tsai, L.: Dual phase-shifting moiré projection with tunable high contrast fringes for three-dimensional microscopic surface profilometry. Phys. Procedia 19, 67–75 (2011)

    Google Scholar 

  92. Wang, J., Liu, F., Wang, Z.: Experimental investigation on the movement mechanism of top coal in steeply inclined ultra-thick coal seams. Acta Mech. Sin. (2021). https://doi.org/10.1007/s10409-020-01044-0

    Article  Google Scholar 

  93. Benbouhenni, H., Boudjema, Z., Belaidi, A.: Using four-level NSVM technique to improve DVC control of a DFIG based wind turbine systems. Period. Polytech. Electr. Eng. Comput. Sci. 63, 144–150 (2019)

    Google Scholar 

  94. Cheng, J.L., Yang, S.Q., Chen, K., et al.: Uniaxial experimental study of the acoustic emission and deformation behavior of composite rock based on 3D digital image correlation (DIC). Acta Mech. Sin. 33, 999–1021 (2017)

    Google Scholar 

  95. Yang, L., Zhong, Z.C., Zhou, Y.C., et al.: Acoustic emission assessment of interface cracking in thermal barrier coatings. Acta Mech. Sin. 32, 342–348 (2016)

    Google Scholar 

  96. Su, Z.L., et al.: Auto-calibration and real-time external parameter correction for stereo digital image correlation. Opt. Lasers Eng. 121, 46–53 (2019)

    Google Scholar 

  97. Liu, Z., Yang, Z., Chen, Y., et al.: Dynamic tensile and failure behavior of bi-directional reinforced GFRP materials. Acta Mech. Sin. 36, 1–12 (2020)

    MathSciNet  Google Scholar 

  98. Xue, Y., et al.: High-accuracy and real-time 3D positioning, tracking system for medical imaging applications based on 3D digital image correlation. Opt. Lasers Eng. 88, 82–90 (2017)

    Google Scholar 

  99. Chen, Z.N., et al.: Noninvasive, three-dimensional full-field body sensor for surface deformation monitoring of human body in vivo. J. Biomed. Opt. 22, 095001 (2017)

    Google Scholar 

  100. Wang, T.Y., et al.: A flexible heterogeneous real-time digital image correlation system. Opt. Lasers Eng. 110, 7–17 (2018)

    Google Scholar 

  101. Yang, D., et al.: Real-time matching strategy for rotary objects using digital image correlation. Appl. Opt. 59, 6648–6657 (2020)

    Google Scholar 

  102. Gembris, D., et al.: Correlation analysis on GPU systems using NVIDIA’s CUDA. J. Real-Time Image Process. 6, 275–280 (2011)

    Google Scholar 

  103. Pan, B., Tian, L.: Superfast robust digital image correlation analysis with parallel computing. Opt. Eng. 54, 034106 (2015)

    Google Scholar 

  104. Wu, R., et al.: Real-time digital image correlation for dynamic strain measurement. Exp. Mech. 56, 833–843 (2016)

    Google Scholar 

  105. Le, B.G., Le Sant, Y., Lévêque, D.: Fast and dense 2D and 3D displacement field estimation by a highly parallel image correlation algorithm. Strain 52, 286–306 (2016)

    Google Scholar 

  106. Shao, X.X., Dai, X.J., He, X.Y.: Noise robustness and parallel computation of the inverse compositional Gauss-Newton algorithm in digital image correlation. Opt. Lasers Eng. 71, 9–19 (2015)

    Google Scholar 

  107. Shao, X., Dai, X., Chen, Z., et al.: Real-time 3D digital image correlation method and its application in human pulse monitoring. Appl. Opt. 55, 696–704 (2016)

    Google Scholar 

  108. Jo, K., Gupta, M., Nayar, S.K.: SpeDo: 6 DOF ego-motion sensor using speckle defocus imaging. In: Proceedings of the IEEE International Conference on Computer Vision (2015)

  109. Zalevsky, Z., et al.: Simultaneous remote extraction of multiple speech sources and heart beats from secondary speckles pattern. Opt Express 17, 21566–21580 (2009)

    Google Scholar 

  110. Li, L., et al.: Vibration measurement by means of digital speckle correlation. In: 2016 International Siberian Conference on Control and Communications (SIBCON). IEEE (2016)

  111. Beiderman, Y., et al.: Remote estimation of blood pulse pressure via temporal tracking of reflected secondary speckles pattern. J. Biomed. Opt. 15, 061707 (2010)

    Google Scholar 

  112. Wu, N., Haruyama, S.: Real-time audio detection and regeneration of moving sound source based on optical flow algorithm of laser speckle images. Opt. Express 28, 4475–4488 (2020)

    Google Scholar 

  113. Song, J.L., et al.: Ultra-high temperature mechanical property test of C/C composites by a digital image correlation method based on an active laser illumination and background radiation suppressing method with multi-step filtering. Appl. Opt. 58, 6569–6580 (2019)

    Google Scholar 

  114. Song, J.L., et al.: High temperature strain measurement method by combining digital image correlation of laser speckle and improved RANSAC smoothing algorithm. Opt. Lasers Eng. 111, 8–18 (2018)

    Google Scholar 

  115. Yu, Q.F., Shang, Y.: Introduction and prospect of videometrics. Sci. Technol. Rev. 26, 84–88 (2008)

    Google Scholar 

  116. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)

    MATH  Google Scholar 

  117. Xu, G., Sugimoto, N.: A linear algorithm for motion from three weak perspective images using Euler angles. Trans. Inst. Electron. Inf. Commun. Eng. 81, 681–688 (1999)

    Google Scholar 

  118. Faig, W.: Calibration of close-range photogrammetry systems: mathematical formulation. Photogramm. Eng. Remote Sens. 41, 1479–1486 (1975)

    Google Scholar 

  119. Abdel-Aziz, Y.I., Karara, H.M.: Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry. In: Proceedings of Symposium on Close-Range Photogrammetry, Urbana, pp. 1–18 (1971)

  120. Wong, K.W.: Mathematical foundation and digital analysis in close-range photogrammetric. Photogramm. Eng. Remote Sens. 44, 1355–1373 (1975)

    Google Scholar 

  121. Tsai, R.Y.: A Versatile camera calibration technique for high-accuracy 3D machine vision metrology using off the shelf TV cameras and lenses. IEEE J. Robot. Autom. 3, 323–344 (1987)

    Google Scholar 

  122. Weng, J., Cohen, P.: Camera calibration with distortion models and accuracy evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 14, 965–980 (1992)

    Google Scholar 

  123. Zhang, Y., Liu, W., Wang, F., et al.: An improved separated-parameter calibration method for binocular vision measurements with large field-of-view. Opt. Express 28, 2956–2974 (2020)

    Google Scholar 

  124. Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1330–1334 (2000)

    Google Scholar 

  125. Zhang, Z.: Camera calibration with one-dimensional objects. IEEE Trans. Pattern Anal. Mach. Intell. 26, 892–899 (2004)

    Google Scholar 

  126. Shang, Y., Yu, Q., Zhang, X.: Analytical method for camera calibration from a single image with four coplanar control lines. Appl. Opt. 43, 5364–5369 (2004)

    Google Scholar 

  127. Maybank, S.J., Faugeras, O.D.: A theory of self-calibration of a moving camera. Int. J. Comput. Vis. 8, 123–151 (1992)

    Google Scholar 

  128. Hartley, R.I.: Estimation of relative camera positions for uncalibrated cameras. In: European Conference on Computer Vision (1992)

  129. Zhang, G.P., Zhao, H., et al.: Robust and flexible method for calibrating the focal length of on-orbit space zoom camera. Appl. Opt. 58, 1467–1474 (2019)

    Google Scholar 

  130. Tang, Z., Lin, Y.S., Lee, K.H., et al.: ESTHER: joint camera self-calibration and automatic radial distortion correction from tracking of walking humans. IEEE Access 7, 1 (2019)

    Google Scholar 

  131. Jin, Z., Yu, H., Deng, H., et al.: A robust and rapid camera calibration method by one captured image. IEEE Trans. Instrum. Meas. 68, 4112–4121 (2019)

    Google Scholar 

  132. Cai, H., Song, Y., Shi, Y., et al.: Flexible multicamera calibration method with a rotating calibration plate. Opt. Express 28, 31397–31413 (2020)

    Google Scholar 

  133. Barreto, J.P., Daniilidis, K.: Fundamental matrix for cameras with radial distortion. In: Tenth IEEE International Conference on Computer Vision (2005)

  134. Guan, B., Yu, Y., Su, A., et al.: Self-calibration approach to stereo cameras with radial distortion based on epipolar constraint. Appl. Opt. 58, 8511 (2019)

    Google Scholar 

  135. Chen, X., Lin, J., Yang, L., et al.: Flexible calibration method for visual measurement using an improved target with vanishing constraints. J. Opt. Soc. Am. A 37, 435–443 (2020)

    Google Scholar 

  136. Cui, J., Min, C., Feng, D.: Research on pose estimation for stereo vision measurement system by an improved method: uncertainty weighted stereopsis pose solution method based on projection vector. Opt. Express 28, 5470–5491 (2020)

    Google Scholar 

  137. Yu, Q.F., Shang, Y., Zhou, J., et al.: Monocular trajectory intersection method for 3D motion measurement of a point target. Sci. China 52, 3454–3463 (2009)

    MATH  Google Scholar 

  138. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. In: Readings in Computer Vision, pp 726–740 (1987)

  139. Wu, F.C., Hu, Z.Y.: A linear method for the PnP problem. J. Softw. 14, 682–688 (2003)

    Google Scholar 

  140. Lepetit, V., Moreno-Noguer, F., Fua, P.: EPnP: an accurate O(n) solution to the PnP problem. Int. J. Comput. Vis. 81, 155–166 (2009)

    Google Scholar 

  141. Li, S., Zhang, Y., Ling, M., et al.: A novel solution to PnP problem for a camera with unknown focal length. In: Fifth International Conference on Computing, Communications and Networking Technologies (2014)

  142. Zhou, B., Chen, Z., Liu, Q.: An efficient solution to the perspective-n-point problem for camera with unknown focal length. IEEE Access 8, 1–1 (2020)

    Google Scholar 

  143. Chris, H., Carl, S.: RAPiD—a video-rate object tracker. In: Proceedings of the British Machine Vision Conference, pp 73–78 (1990)

  144. Choi, C., Christensen, H.I.: Real-time 3D model-based tracking using edge and keypoint features for robotic manipulation. In: IEEE International Conference on Robotics and Automation (2010)

  145. Prisacariu, V.A.: PWP3D: real-time segmentation and tracking of 3D objects. Int. J. Comput. Vis. 98, 335–354 (2012)

    MathSciNet  Google Scholar 

  146. Zhong, L., Zhang, L.: A robust monocular 3D object tracking method combining statistical and photometric constraints. Int. J. Comput. Vis. 127, 973–992 (2019)

    Google Scholar 

  147. Mundy, J.L.: Object recognition in the geometric era: a retrospective. In: Toward Category-Level Object Recognition. Springer, Berlin (2006)

  148. Hinterstoisser, S., Holzer, S., Cagniart, C., et al.: Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes. In: IEEE International Conference on Computer Vision (2012)

  149. Cao, Z., Sheikh, Y., Banerjee, N.K.: Real-time scalable 6DOF pose estimation for textureless objects. In: IEEE International Conference on Robotics and Automation (2016)

  150. Lim, J.J., Pirsiavash, H., Torralba, A.: Parsing IKEA objects: fine pose estimation. In: Proceedings of the 2013 IEEE International Conference on Computer Vision (2014)

  151. Choy, C.B., Stark, M., Corbett-Davies, S., et al.: Enriching object detection with 2D–3D registration and continuous viewpoint estimation. In: Computer Vision and Pattern Recognition (2015)

  152. Mottaghi, R., Xiang, Y., et al.: A coarse-to-fine model for 3D pose estimation and sub-category recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (2015)

  153. Peng, S., Zhou, X., Liu, Y., et al.: PVNet: pixel-wise voting network for 6DoF object pose estimation. IEEE Trans. Pattern Anal. Mach. Intell. 99, 1 (2020)

    Google Scholar 

  154. Li, Z., Wang, G., Ji, X.: CDPN: coordinates-based disentangled pose network for real-time RGB-based 6-DoF object pose estimation. IEEE International Conference on Computer Vision (2019)

  155. Jiang, R., David, V.J., White, K.R.: Close-range photogrammetry applications in bridge measurement: literature review. Measurement 41, 823–834 (2008)

    Google Scholar 

  156. Baqersad, J., Poozesh, P., et al.: Photogrammetry and optical methods in structural dynamics—a review. Mech. Syst. Signal Process. 86, 17–34 (2017)

    Google Scholar 

  157. Galantucci, L.M., Guerra, M.G., Lavecchia, F.: Photogrammetry applied to small and micro scaled objects: a review. In: Proceedings of 3rd International Conference on the Industry 4.0 Model for Advanced Manufacturing (2018)

  158. Xu, Y., Brownjohn, J.M.W.: Review of machine-vision based methodologies for displacement measurement in civil structures. J. Civ. Struct. Health Monit. 8, 91–110 (2018)

    Google Scholar 

  159. Shang, Y., Yu, Q., Guan, B., et al.: Recent advances of videometrics for large-scale structure deformation monitoring. J. Exp. Mech. 5, 593–600 (2017)

    Google Scholar 

  160. Yaryshev, S.N., Li, L., et al.: Development of a digital camera-based method for bridge deformation measurement. In: 2020 XXIX International Scientific Conference Electronics (2020)

  161. Guerra, F., Haist, T., Warsewa, A., et al.: Precise building deformation measurement using holographic multipoint replication. Appl. Opt. 59, 2746 (2020)

    Google Scholar 

  162. Olaszek, P.: Investigation of the dynamic characteristic of bridge structures using a computer vision method. Measurement 25, 227–236 (1999)

    Google Scholar 

  163. Yu, Q., Shang, Y., Guan, B., et al.: Camera series and parallel networks for deformation measurements of large scale structures. In: Proceedings of SPIE. The International Society for Optical Engineering (2015)

  164. Liu, X., Tong, X., Yin, X., et al.: Videogrammetric technique for three-dimensional structural progressive collapse measurement. Measurement 63, 87–99 (2015)

    Google Scholar 

  165. Black, J.T., Pitcher, N.A., Reeder, M.F., et al.: Videogrammetry dynamics measurements of a lightweight flexible wing in a wind tunnel. J. Aircr. 47, 172–180 (2010)

    Google Scholar 

  166. Kalpoe, D., Khoshelham, K., Gorte, B.: Vibration measurement of a model wind turbine using high speed photogrammetry. In: Proceedings of SPIE. The International Society for Optical Engineering (2011)

  167. Ozbek, M., Meng, F., Rixen, D.J.: Challenges in testing and monitoring the in-operation vibration characteristics of wind turbines. Mech. Syst. Signal Process. 41, 649–666 (2013)

    Google Scholar 

  168. Chen, C.C., Wu, W.H., Tseng, H.Z., et al.: Application of digital photogrammetry techniques in identifying the mode shape ratios of stay cables with multiple camcorders. Measurement 75, 134–146 (2015)

    Google Scholar 

  169. De, M.Q., Lefebvre-Albaret, F., Basarab, A., et al.: Wing 3D reconstruction by constraining the bundle adjustment with mechanical limitations. In: 28th European Signal Processing Conference (2021)

  170. Morlier, J., Salom, P., Bos, F.: New image processing tools for structural dynamic monitoring. Key Eng. Mater. 347, 239–244 (2007)

    Google Scholar 

  171. Kuddus, M.A., Li, J., Hao, H., et al.: Target-free vision-based technique for vibration measurements of structures subjected to out-of-plane movements. Eng. Struct. 190, 210–222 (2019)

    Google Scholar 

  172. Son, K.S., Jeon, H.S., Chae, G.S., et al.: A fast high-resolution vibration measurement method based on vision technology for structures. Nucl. Eng. Technol. 53, 294–303 (2020)

    Google Scholar 

  173. Ji, Y.F., Chang, C.C.: Nontarget image-based technique for small cable vibration measurement. J. Bridge Eng. 13, 34–42 (2008)

    Google Scholar 

  174. Kim, S.W., Kim, N.S.: Dynamic characteristics of suspension bridge hanger cables using digital image processing. NDT E Int. 59, 25–33 (2013)

    Google Scholar 

  175. Bartilson, D.T., Wieghaus, K.T., Hurlebaus, S.: Target-less computer vision for traffic signal structure vibration studies. Mech. Syst. Signal Process. 60–61, 571–582 (2015)

    Google Scholar 

  176. Yang, Y.C., Dorn, C., et al.: Blind, simultaneous identification of full-field vibration modes and large rigid-body motion of output-only structures from digital video measurements. Eng. Struct. 207, 110183 (2020)

    Google Scholar 

  177. Feng, M.Q., Leung, R.Y.: Application of computer vision for estimation of moving vehicle weight. IEEE Sens. J. 99, 1 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qifeng Yu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Executive Editor: Yuejie Wei.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fu, Y., Shang, Y., Hu, W. et al. Non-contact optical dynamic measurements at different ranges: a review. Acta Mech. Sin. 37, 537–553 (2021). https://doi.org/10.1007/s10409-021-01102-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10409-021-01102-1

Keywords

Navigation